Current Evidence and Future Directions for Colchicine in the Prevention of Atherosclerotic Cardiovascular Disease
Bibliographic record
Abstract
Chronic inflammation plays a key role in the development and progression of atherosclerotic cardiovascular disease (ASCVD) and its complications. Despite the use of blood pressure-, lipid-, and glucose-lowering therapies as well as antithrombotic agents, the lifetime residual cardiovascular (CV) risk in patients with ASCVD remains high. Because chronic inflammation remains an unaddressed risk factor, anti-inflammatory therapy has the potential to further lower residual CV risk in these patients. Low-dose colchicine (0.5 mg daily) has emerged as a promising low-cost oral anti-inflammatory therapy for this indication. In patients with chronic coronary syndrome (CCS), low-dose colchicine was well-tolerated and reduced the risk of myocardial infarction, stroke, coronary revascularization, and CV death. However, trials in patients with acute coronary syndrome (ACS) yielded conflicting results, and two trials in patients with ischemic stroke did not show a benefit. In patients with peripheral artery disease (PAD), preliminary observational data suggested a potential benefit, and a randomized trial is currently underway to examine its efficacy in reducing CV and limb events. The long-term safety data for low-dose colchicine in ASCVD are reassuring. Although pooled data from trials in ASCVD show a small (0.55%) absolute increase in the risk of hospitalization for gastrointestinal events, adverse signals were not observed for serious infection, cancer, or severe myotoxicity. In this article, we review the clinical studies of colchicine that examined its risk-benefit for the prevention of CV events in patients with ASCVD, discuss clinical and research implications, and highlight knowledge gaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".